Laurent Charlin

Core Academic Member
Laurent Charlin
Assistant Professor, HEC Montréal
Laurent Charlin

I am a assistant professor in the department of decision sciences at HEC (and adjunct at the DIRO). I develop novel machine learning models, particularly probabilistic graphical models, to help in decision making. My recent work has focused on extending the capabilities of recommender systems.

I am generally interested in applying learning methods to analyze different data.

I have co-developed the Toronto Paper Matching System (TPMS) which is a tool to assist conference organizers match their reviewers to submitted papers. The system is now online and has been used by large machine learning and computer vision conferences over the last few years.

I graduated with a PhD from the University of Toronto where I worked with Rich Zemel and Craig Boutilier. I also have a Master’s from U. Waterloo where I was supervised by Pascal Poupart. Finally, I did postdocs at Princeton and Columbia under the supervision of David Blei and at McGill with Joelle Pineau.

Publications

2020-11

Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles
Yao Lu, Yue Dong and Laurent Charlin
EMNLP 2020
(2020-11-01)

2020-10

Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles
Yao Lu, Yue Dong and Laurent Charlin
arXiv preprint arXiv:2010.14235
(2020-10-27)
arxiv.orgPDF

2020-09

Causal Inference for Recommender Systems
Yixin Wang, Dawen Liang, Laurent Charlin and David M. Blei
RECSYS 2020
(2020-09-22)
dl.acm.org
Synbols: Probing Learning Algorithms with Synthetic Datasets
Alexandre Lacoste, Pau Rodríguez, Frédéric Branchaud-Charron, Parmida Atighehchian, Massimo Caccia, Issam H. Laradji, Alexandre Drouin, Matt Craddock, Laurent Charlin and David Vázquez
NEURIPS 2020
(2020-09-14)
papers.nips.cc
Synbols: Probing Learning Algorithms with Synthetic Datasets
Alexandre Lacoste, Pau Rodríguez, Frédéric Branchaud-Charron, Parmida Atighehchian, Massimo Caccia, Issam Laradji, Alexandre Drouin, Matt Craddock, Laurent Charlin and David Vázquez
arXiv e-prints
(2020-09-14)
ui.adsabs.harvard.eduPDF
On the Effectiveness of Two-Step Learning for Latent-Variable Models
Cem Subakan, Maxime Gasse and Laurent Charlin
MLSP 2020
(2020-09-01)
xplorestaging.ieee.org

2020-07

A Large-Scale, Open-Domain, Mixed-Interface Dialogue-Based ITS for STEM
Iulian Vlad Serban, Varun Gupta, Ekaterina Kochmar, Dung Do Vu, Robert Belfer, Joelle Pineau, Aaron C. Courville, Laurent Charlin and Yoshua Bengio

2020-04

Language GANs Falling Short
Massimo Caccia, Lucas Caccia, William Fedus, Hugo Larochelle, Joelle Pineau and Laurent Charlin
Predictive inference for travel time on transportation networks.
Mohamad Elmasri, Aurelie Labbe, Denis Larocque and Laurent Charlin
arXiv preprint arXiv:2004.11292
(2020-04-23)
aps.arxiv.orgPDF
Prediction intervals for travel time on transportation networks
Mohamad Elmasri, Aurelie Labbe, Denis Larocque and Laurent Charlin
(venue unknown)
(2020-04-23)
aps.arxiv.orgPDF
Inference for travel time on transportation networks
Mohamad Elmasri, Aurelie Labbe, Denis Larocque and Laurent Charlin
arXiv preprint arXiv:2004.11292
(2020-04-23)
aps.arxiv.orgPDF

2020-03

Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning.
Massimo Caccia, Pau Rodríguez, Oleksiy Ostapenko, Fabrice Normandin, Min Lin, Lucas Caccia, Issam H. Laradji, Irina Rish, Alexandre Lacoste, David Vázquez and Laurent Charlin
arXiv preprint arXiv:2003.05856
(2020-03-12)
ui.adsabs.harvard.eduPDF
IG-RL: Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal Control
François-Xavier Devailly, Denis Larocque and Laurent Charlin
arXiv preprint arXiv:2003.05738
(2020-03-06)
ui.adsabs.harvard.eduPDF
Online Fast Adaptation and Knowledge Accumulation (OSAKA): a New Approach to Continual Learning
Massimo Caccia, Pau Rodriguez, Oleksiy Ostapenko, Fabrice Normandin, Min Lin, Lucas Page-Caccia, Issam Hadj Laradji, Irina Rish, Alexandre Lacoste, David Vázquez and Laurent Charlin
NEURIPS 2020
(2020-03-01)
papers.nips.cc

2019-12

Online Continual Learning with Maximal Interfered Retrieval
Rahaf Aljundi, Eugene Belilovsky, Tinne Tuytelaars, Laurent Charlin, Massimo Caccia, Min Lin and Lucas Page-Caccia
NEURIPS 2019
(2019-12-08)
papers.nips.ccPDF
Exact Combinatorial Optimization with Graph Convolutional Neural Networks
Maxime Gasse, Didier Chetelat, Nicola Ferroni, Laurent Charlin and Andrea Lodi

2019-10

Continual Learning of New Sound Classes Using Generative Replay
Zhepei Wang, Cem Subakan, Efthymios Tzinis, Paris Smaragdis and Laurent Charlin

2019-08

Online Continual Learning with Maximally Interfered Retrieval.
Rahaf Aljundi, Lucas Caccia, Eugene Belilovsky, Massimo Caccia, Min Lin, Laurent Charlin and Tinne Tuytelaars
arXiv preprint arXiv:1908.04742
(2019-08-11)
arxiv.org

2019-01

Session-Based Social Recommendation via Dynamic Graph Attention Networks
Weiping Song, Zhiping Xiao, Yifan Wang, Laurent Charlin, Ming Zhang and Jian Tang

2018-12

Towards Deep Conversational Recommendations
Raymond Li, Samira Ebrahimi Kahou, Hannes Schulz, Vincent Michalski, Laurent Charlin and Chris Pal

2018-07

Focused Hierarchical RNNs for Conditional Sequence Processing
Nan Ke, Konrad Zolna, Alessandro Sordoni, Mila Zhouhan Lin, Yoshua Bengio, Joelle Pineau, Laurent Charlin and Christopher Pal
ICML 2018
(2018-07-10)
proceedings.mlr.pressPDF

2018-06

Focused Hierarchical RNNs for Conditional Sequence Processing
Nan Rosemary Ke, Konrad Zolna, Alessandro Sordoni, Zhouhan Lin, Adam Trischler, Yoshua Bengio, Joelle Pineau, Laurent Charlin and Chris Pal
arXiv preprint arXiv:1806.04342
(2018-06-12)
aps.arxiv.orgPDF
A Survey of Available Corpora For Building Data-Driven Dialogue Systems: The Journal Version
Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin and Joelle Pineau
Dialogue & Discourse
(2018-06-01)
dad.uni-bielefeld.dePDF

2018-02

Sparse Attentive Backtracking: Long-Range Credit Assignment in Recurrent Networks
Nan Rosemary Ke, Anirudh Goyal, Olexa Bilaniuk, Jonathan Binas, Laurent Charlin, Chris Pal and Yoshua Bengio
arXiv preprint arXiv:1711.02326
(2018-02-15)
ui.adsabs.harvard.eduPDF

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